An extension of Separable Lattice 2-D HMMS for rotational data variations

This paper proposes a new generative model which can deal with rotational data variations by extending Separable Lattice 2-D HMMs (SL2D-HMMs). In image recognition, geometrical variations such as size, location and rotation degrade the performance, therefore normalization is required. SL2D-HMMs can perform an elastic matching in both horizontal and vertical directions; this makes it possible to model invariances to size and location. To deal with rotational variations, we introduce additional HMM states which represent the shifts of the state alignments of the observation lines in a particular direction. Face recognition experiments show that the proposed method improves the performance significantly for rotational variation data.